MetaboNet: The Largest Publicly Available Consolidated Dataset for Type 1 Diabetes Management

2026-01-16

AI Summary

This work addresses the limitations in current type 1 diabetes management algorithm research—namely, fragmented, structurally inconsistent, and inaccessible datasets—that hinder algorithm comparability and generalizability. To overcome these challenges, we introduce MetaboNet, the first large-scale, standardized, and publicly available multimodal dataset for type 1 diabetes. It integrates continuous glucose monitoring (CGM) and insulin pump dosing data from 3,135 participants, amounting to 1,228 patient-years, while preserving auxiliary information such as carbohydrate intake and physical activity. The project provides an automated data formatting pipeline and a controlled-access mechanism, including a Data Use Agreement (DUA), substantially enhancing data accessibility and supporting the development of more generalizable, reproducible diabetes management algorithms.

Technology Category

• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

Progress in Type 1 Diabetes (T1D) algorithm development is limited by the fragmentation and lack of standardization across existing T1D management datasets. Current datasets differ substantially in structure and are time-consuming to access and process, which impedes data integration and reduces the comparability and generalizability of algorithmic developments. This work aims to establish a unified and accessible data resource for T1D algorithm development. Multiple publicly available T1D datasets were consolidated into a unified resource, termed the MetaboNet dataset. Inclusion required the availability of both continuous glucose monitoring (CGM) data and corresponding insulin pump dosing records. Additionally, auxiliary information such as reported carbohydrate intake and physical activity was retained when present. The MetaboNet dataset comprises 3135 subjects and 1228 patient-years of overlapping CGM and insulin data, making it substantially larger than existing standalone benchmark datasets. The resource is distributed as a fully public subset available for immediate download at https://metabo-net.org/ , and with a Data Use Agreement (DUA)-restricted subset accessible through their respective application processes. For the datasets in the latter subset, processing pipelines are provided to automatically convert the data into the standardized MetaboNet format. A consolidated public dataset for T1D research is presented, and the access pathways for both its unrestricted and DUA-governed components are described. The resulting dataset covers a broad range of glycemic profiles and demographics and thus can yield more generalizable algorithmic performance than individual datasets.

Problem

• Type 1 Diabetes
• dataset fragmentation
• standardization
• algorithm development
• data integration

Innovation

• MetaboNet
• Type 1 Diabetes
• data standardization
• continuous glucose monitoring
• insulin pump data